{
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  "metadata": {
    "colab": {
      "name": "Exploratory data Analysis.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOvht7vqQGdR",
        "colab_type": "text"
      },
      "source": [
        "# Exploratory data analysis in Python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhA_0CQOTDQy",
        "colab_type": "text"
      },
      "source": [
        "## Let us understand how to explore the data in python.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEfC0QszTKX_",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "![alt text](https://moriohcdn.b-cdn.net/ff3cc511fb.png)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMyUUHXcHdqt",
        "colab_type": "text"
      },
      "source": [
        "Image Credits: Morioh"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dB_j6LtTTO5j",
        "colab_type": "text"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hg00soETQ3z",
        "colab_type": "text"
      },
      "source": [
        "**What is Exploratory Data Analysis ?**\n",
        "\n",
        "Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics often plotting them visually. This step is very important especially when we arrive at modeling the data in order to apply Machine learning. Plotting in EDA consists of Histograms, Box plot, Scatter plot and many more. It often takes much time to explore the data. Through the process of EDA, we can ask to define the problem statement or definition on our data set which is very important."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfelutoyTS25",
        "colab_type": "text"
      },
      "source": [
        "**How to perform Exploratory Data Analysis ?**\n",
        "\n",
        "This is one such question that everyone is keen on knowing the answer. Well, the answer is it depends on the data set that you are working. There is no one method or common methods in order to perform EDA, whereas in this tutorial you can understand some common methods and plots that would be used in the EDA process."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3VfNkBBw15s",
        "colab_type": "text"
      },
      "source": [
        "**What data are we exploring today ?**\n",
        "\n",
        "\n",
        "\n",
        "Since I am a huge fan of cars, I got a very beautiful data-set of cars from Kaggle. The data-set can be downloaded from [here](https://www.kaggle.com/CooperUnion/cardataset). To give a piece of brief information about the data set this data contains more of 10, 000 rows and more than 10 columns which contains features of the car such as Engine Fuel Type, Engine HP, Transmission Type, highway MPG, city MPG and many more. So in this tutorial, we will explore the data and make it ready for modeling."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQDO4JCqTThV",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPSqz1wzTXvz",
        "colab_type": "text"
      },
      "source": [
        "## 1. Importing the required libraries for EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eLMx1Ebwa92",
        "colab_type": "text"
      },
      "source": [
        "Below are the libraries that are used in order to perform EDA (Exploratory data analysis) in this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGyDovL2QDLa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns                       #visualisation\n",
        "import matplotlib.pyplot as plt             #visualisation\n",
        "%matplotlib inline     \n",
        "sns.set(color_codes=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ye6eXuohTd5Q",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Y3Z2DbKTfJt",
        "colab_type": "text"
      },
      "source": [
        "## 2. Loading the data into the data frame."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ko5zGJFCySaz",
        "colab_type": "text"
      },
      "source": [
        "Loading the data into the pandas data frame is certainly one of the most important steps in EDA, as we can see that the value from the data set is comma-separated. So all we have to do is to just read the CSV into a data frame and pandas data frame does the job for us."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgzUzD61IM8h",
        "colab_type": "text"
      },
      "source": [
        "To get or load the dataset into the notebook, all I did was one trivial step. In Google Colab at the left-hand side of the notebook, you will find a > (greater than symbol). When you click that you will find a tab with three options, you just have to select Files. Then you can easily upload your file with the help of the Upload option. No need to mount to the google drive or use any specific libraries just upload the data set and your job is done. One thing to remember in this step is that uploaded files will get deleted when this runtime is recycled. This is how I got the data set into the notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oVZnezwQ159",
        "colab_type": "code",
        "outputId": "f1e0fe18-8fa0-482a-e2b9-2ecd87d97d9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "source": [
        "df = pd.read_csv(\"data.csv\")\n",
        "# To display the top 5 rows \n",
        "df.head(5)               "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine Fuel Type</th>\n",
              "      <th>Engine HP</th>\n",
              "      <th>Engine Cylinders</th>\n",
              "      <th>Transmission Type</th>\n",
              "      <th>Driven_Wheels</th>\n",
              "      <th>Number of Doors</th>\n",
              "      <th>Market Category</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>highway MPG</th>\n",
              "      <th>city mpg</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Factory Tuner,Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>3916</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Make       Model  Year  ... city mpg  Popularity   MSRP\n",
              "0  BMW  1 Series M  2011  ...       19        3916  46135\n",
              "1  BMW    1 Series  2011  ...       19        3916  40650\n",
              "2  BMW    1 Series  2011  ...       20        3916  36350\n",
              "3  BMW    1 Series  2011  ...       18        3916  29450\n",
              "4  BMW    1 Series  2011  ...       18        3916  34500\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fm-9dzdTRKpe",
        "colab_type": "code",
        "outputId": "7892eaf7-0605-4b92-e139-cf0553041e51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        }
      },
      "source": [
        "df.tail(5)                        # To display the botton 5 rows"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine Fuel Type</th>\n",
              "      <th>Engine HP</th>\n",
              "      <th>Engine Cylinders</th>\n",
              "      <th>Transmission Type</th>\n",
              "      <th>Driven_Wheels</th>\n",
              "      <th>Number of Doors</th>\n",
              "      <th>Market Category</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>highway MPG</th>\n",
              "      <th>city mpg</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11909</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>46120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11910</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>56670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11911</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>50620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11912</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2013</td>\n",
              "      <td>premium unleaded (recommended)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>50920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11913</th>\n",
              "      <td>Lincoln</td>\n",
              "      <td>Zephyr</td>\n",
              "      <td>2006</td>\n",
              "      <td>regular unleaded</td>\n",
              "      <td>221.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>front wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>61</td>\n",
              "      <td>28995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Make   Model  Year  ... city mpg  Popularity   MSRP\n",
              "11909    Acura     ZDX  2012  ...       16         204  46120\n",
              "11910    Acura     ZDX  2012  ...       16         204  56670\n",
              "11911    Acura     ZDX  2012  ...       16         204  50620\n",
              "11912    Acura     ZDX  2013  ...       16         204  50920\n",
              "11913  Lincoln  Zephyr  2006  ...       17          61  28995\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JjQnr4SPzaL5",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAmC369yTpMF",
        "colab_type": "text"
      },
      "source": [
        "## 3. Checking the types of data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ESKxikIzA1d",
        "colab_type": "text"
      },
      "source": [
        "Here we check for the datatypes because sometimes the MSRP or the price of the car would be stored as a string, if in that case, we have to convert that string to the integer data only then we can plot the data via a graph. Here, in this case, the data is already in integer format so nothing to worry."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPbKQ0noRptD",
        "colab_type": "code",
        "outputId": "6a5aea47-ad0c-4118-8471-d91b6432b339",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make                  object\n",
              "Model                 object\n",
              "Year                   int64\n",
              "Engine Fuel Type      object\n",
              "Engine HP            float64\n",
              "Engine Cylinders     float64\n",
              "Transmission Type     object\n",
              "Driven_Wheels         object\n",
              "Number of Doors      float64\n",
              "Market Category       object\n",
              "Vehicle Size          object\n",
              "Vehicle Style         object\n",
              "highway MPG            int64\n",
              "city mpg               int64\n",
              "Popularity             int64\n",
              "MSRP                   int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beRJyQAezdX8",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QoHuBhXxT5E9",
        "colab_type": "text"
      },
      "source": [
        "## 4. Dropping irrelevant columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3cy877Mze4H",
        "colab_type": "text"
      },
      "source": [
        "This step is certainly needed in every EDA because sometimes there would be many columns that we never use in such cases dropping is the only solution. In this case, the columns such as Engine Fuel Type, Market Category, Vehicle style, Popularity, Number of doors, Vehicle Size doesn't make any sense to me so I just dropped for this instance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvSkK8swTr9H",
        "colab_type": "code",
        "outputId": "1734f538-a4a1-45b9-d656-7ddc0124dc35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df = df.drop(['Engine Fuel Type', 'Market Category', 'Vehicle Style', 'Popularity', 'Number of Doors', 'Vehicle Size'], axis=1)\n",
        "df.head(5)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine HP</th>\n",
              "      <th>Engine Cylinders</th>\n",
              "      <th>Transmission Type</th>\n",
              "      <th>Driven_Wheels</th>\n",
              "      <th>highway MPG</th>\n",
              "      <th>city mpg</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Make       Model  Year  ...  highway MPG  city mpg   MSRP\n",
              "0  BMW  1 Series M  2011  ...           26        19  46135\n",
              "1  BMW    1 Series  2011  ...           28        19  40650\n",
              "2  BMW    1 Series  2011  ...           28        20  36350\n",
              "3  BMW    1 Series  2011  ...           28        18  29450\n",
              "4  BMW    1 Series  2011  ...           28        18  34500\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20OeQBpWz89v",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caAownWdUZso",
        "colab_type": "text"
      },
      "source": [
        "## 5. Renaming the columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UABLiEcyz-2G",
        "colab_type": "text"
      },
      "source": [
        "In this instance, most of the column names are very confusing to read, so I just tweaked their column names. This is a good approach it improves the readability of the data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wHW41whURub",
        "colab_type": "code",
        "outputId": "dec9b1b3-e344-4b33-92fd-6e9f4c03c878",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df = df.rename(columns={\"Engine HP\": \"HP\", \"Engine Cylinders\": \"Cylinders\", \"Transmission Type\": \"Transmission\", \"Driven_Wheels\": \"Drive Mode\",\"highway MPG\": \"MPG-H\", \"city mpg\": \"MPG-C\", \"MSRP\": \"Price\" })\n",
        "df.head(5)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>HP</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Drive Mode</th>\n",
              "      <th>MPG-H</th>\n",
              "      <th>MPG-C</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Make       Model  Year     HP  ...        Drive Mode MPG-H MPG-C  Price\n",
              "0  BMW  1 Series M  2011  335.0  ...  rear wheel drive    26    19  46135\n",
              "1  BMW    1 Series  2011  300.0  ...  rear wheel drive    28    19  40650\n",
              "2  BMW    1 Series  2011  300.0  ...  rear wheel drive    28    20  36350\n",
              "3  BMW    1 Series  2011  230.0  ...  rear wheel drive    28    18  29450\n",
              "4  BMW    1 Series  2011  230.0  ...  rear wheel drive    28    18  34500\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tcGiOmV0afN",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiW7x_O4WIDX",
        "colab_type": "text"
      },
      "source": [
        "## 6. Dropping the duplicate rows"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LpR5NW70hXm",
        "colab_type": "text"
      },
      "source": [
        "This is often a handy thing to do because a huge data set as in this case contains more than 10, 000 rows often have some duplicate data which might be disturbing, so here I remove all the duplicate value from the data-set. For example prior to removing I had 11914 rows of data but after removing the duplicates 10925 data meaning that I had 989 of duplicate data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nBN_BCDWSmv",
        "colab_type": "code",
        "outputId": "9a070a7d-a4d4-45c7-cac8-acb2c4db0e72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11914, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yB8t6o0wH7If",
        "colab_type": "code",
        "outputId": "d4778cd1-5372-4e27-db62-20635493786e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "duplicate_rows_df = df[df.duplicated()]\n",
        "print(\"number of duplicate rows: \", duplicate_rows_df.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of duplicate rows:  (989, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chhNvMFCIzqI",
        "colab_type": "text"
      },
      "source": [
        "Now let us remove the duplicate data because it's ok to remove them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kuobmetTV820",
        "colab_type": "code",
        "outputId": "c6d6ce5c-6a38-4cd2-ee99-151124a1f84d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "df.count()      # Used to count the number of rows"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make            11914\n",
              "Model           11914\n",
              "Year            11914\n",
              "HP              11845\n",
              "Cylinders       11884\n",
              "Transmission    11914\n",
              "Drive Mode      11914\n",
              "MPG-H           11914\n",
              "MPG-C           11914\n",
              "Price           11914\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MJKjbzHI40K",
        "colab_type": "text"
      },
      "source": [
        "So seen above there are 11914 rows and we are removing 989 rows of duplicate data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OiOsEF6WVTSj",
        "colab_type": "code",
        "outputId": "a527c1a2-5d74-42bb-99e2-8112ebffb871",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df = df.drop_duplicates()\n",
        "df.head(5)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>HP</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Drive Mode</th>\n",
              "      <th>MPG-H</th>\n",
              "      <th>MPG-C</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Make       Model  Year     HP  ...        Drive Mode MPG-H MPG-C  Price\n",
              "0  BMW  1 Series M  2011  335.0  ...  rear wheel drive    26    19  46135\n",
              "1  BMW    1 Series  2011  300.0  ...  rear wheel drive    28    19  40650\n",
              "2  BMW    1 Series  2011  300.0  ...  rear wheel drive    28    20  36350\n",
              "3  BMW    1 Series  2011  230.0  ...  rear wheel drive    28    18  29450\n",
              "4  BMW    1 Series  2011  230.0  ...  rear wheel drive    28    18  34500\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2gMM4lb0Vzor",
        "colab_type": "code",
        "outputId": "f04e1803-e7de-4cbf-fdeb-8449a8051a07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "df.count()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make            10925\n",
              "Model           10925\n",
              "Year            10925\n",
              "HP              10856\n",
              "Cylinders       10895\n",
              "Transmission    10925\n",
              "Drive Mode      10925\n",
              "MPG-H           10925\n",
              "MPG-C           10925\n",
              "Price           10925\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCLUdZOQ1PDP",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkXUQtyQW3Dy",
        "colab_type": "text"
      },
      "source": [
        "## 7. Dropping the missing or null values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5pKvJi41YCp",
        "colab_type": "text"
      },
      "source": [
        "This is mostly similar to the previous step but in here all the missing values are detected and are dropped later. Now, this is not a good approach to do so, because many people just replace the missing values with the mean or the average of that column, but in this case, I just dropped that missing values. This is because there is nearly 100 missing value compared to 10, 000 values this is a small number and this is negligible so I just dropped those values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tzdlg-1OWjMz",
        "colab_type": "code",
        "outputId": "7375ab6e-1473-4346-e5b5-0c61189cc716",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "print(df.isnull().sum())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Make             0\n",
            "Model            0\n",
            "Year             0\n",
            "HP              69\n",
            "Cylinders       30\n",
            "Transmission     0\n",
            "Drive Mode       0\n",
            "MPG-H            0\n",
            "MPG-C            0\n",
            "Price            0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWJqTVxTJQnO",
        "colab_type": "text"
      },
      "source": [
        "This is the reason in the above step while counting both Cylinders and Horsepower (HP) had 10856 and 10895 over 10925 rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbbV0xHPWoad",
        "colab_type": "code",
        "outputId": "17dda8ec-1282-4814-de79-8f5e1aff3a5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "df = df.dropna()    # Dropping the missing values.\n",
        "df.count()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make            10827\n",
              "Model           10827\n",
              "Year            10827\n",
              "HP              10827\n",
              "Cylinders       10827\n",
              "Transmission    10827\n",
              "Drive Mode      10827\n",
              "MPG-H           10827\n",
              "MPG-C           10827\n",
              "Price           10827\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2t7L9l2mJSoX",
        "colab_type": "text"
      },
      "source": [
        "Now we have removed all the rows which contain the Null or N/A values (Cylinders and Horsepower (HP))."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-DmX1O4Wtox",
        "colab_type": "code",
        "outputId": "2d50fc20-3535-413b-e317-75a7f94fa2a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "print(df.isnull().sum())   # After dropping the values"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Make            0\n",
            "Model           0\n",
            "Year            0\n",
            "HP              0\n",
            "Cylinders       0\n",
            "Transmission    0\n",
            "Drive Mode      0\n",
            "MPG-H           0\n",
            "MPG-C           0\n",
            "Price           0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bk8RAHqQJVJK",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Py3sQc_ZxyU",
        "colab_type": "text"
      },
      "source": [
        "## 8. Detecting Outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1g6CJPofZzHN",
        "colab_type": "text"
      },
      "source": [
        "An outlier is a point or set of points that are different from other points. Sometimes they can be very high or very low. It's often a good idea to detect and remove the outliers. Because outliers are one of the primary reasons for resulting in a less accurate model. Hence it's a good idea to remove them. The outlier detection and removing that I am going to perform is called IQR score technique. Often outliers can be seen with visualizations using a box plot. Shown below are the box plot of MSRP, Cylinders, Horsepower and EngineSize. Herein all the plots, you can find some points are outside the box they are none other than outliers. The technique of finding and removing outlier that I am performing in this assignment is taken help of a tutorial from[ towards data science](https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtxX_y6zZ2ri",
        "colab_type": "code",
        "outputId": "e3f93522-9244-4c32-c34c-103e0834e93b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "sns.boxplot(x=df['Price'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d36a38be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PHvTj0oCscCcN0xoaGjRv3ryrnh/sV4kmk0mVlpZe9XxLS4va29s1d+7czHOpVKrPY8Aa\nIo0bUiAQGHBbNBpVIpHo9/lbbrlFH3300XAODcgrPu7AiFNdXa3XXntNjY2Ncs6pqalJLS0tmj17\ntsaNG6ddu3apu7tbqVRKx48f17fffuv3kIEBcSeNEWfZsmXq6urS008/rfb2dhUXF+uFF15QcXGx\ndu7cqXg8rgceeEAXL17U9OnT9dRTT/k9ZGBA/GUWADCMjzsAwDAiDQCGEWkAMIxIA4BhRBoADCPS\nAGAYkQYAw4g0ABhGpAHAsP8H5yM8SLxc/FgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HtvQYVHZ8u5",
        "colab_type": "code",
        "outputId": "3dc30a01-6fb1-41d9-dec8-0ceeead6a358",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "sns.boxplot(x=df['HP'])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d369b3ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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69WpNnTpV1dXVKiws1I8//qgHH3xQPp9PI0aMkKROx+LNjh079N5778njaf4PIqtWrdL0\n6dO79fo3bNignTt36vz58+rVq5cyMzP12Wefhb3mrjgfRBoADON2BwAYRqQBwDAiDQCGEWkAMIxI\nA4BhRBoADCPSiEs5OTnav39/m+fKy8s1f/781vFx48bp0Ucf1aRJk1RYWKj6+vpYTBW4J0Qa3VZZ\nWZmOHj2qTz/9VJWVlSotLY31lIC7RqTR7fXv319PPfWUqqqqYj0V4K4RaXR7dXV12rNnj0aPHh3r\nqQB3jW/BQ9xavnx563f3SlJjY6PGjBnTbrxnz56aOnWqXnnllVhME7gnRBpxq6SkRJMmTWp9XF5e\nrk8++aTDcSAecbsDAAwj0gBgGJEGAMP4PmkAMIwraQAwjEgDgGFEGgAMI9IAYBiRBgDDiDQAGEak\nAcAwIg0AhhFpADDs/wCR8VlxtL5a4gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xz3MwIjbaBUr",
        "colab_type": "code",
        "outputId": "335d1e55-55a3-4e61-8401-a414580d9d62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "sns.boxplot(x=df['Cylinders'])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d3413ff28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+v19VVVWqra1t9PFRo0aprKxM5eXlKi4uVseOHTVgwIDr2vfV4Y2MjNTFixclSadOnVJc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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFG9hck7aHUx",
        "colab_type": "code",
        "outputId": "7c09ff98-3725-4d56-9b97-ef6b43d0e17b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "Q1 = df.quantile(0.25)\n",
        "Q3 = df.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "print(IQR)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Year             9.0\n",
            "HP             130.0\n",
            "Cylinders        2.0\n",
            "MPG-H            8.0\n",
            "MPG-C            6.0\n",
            "Price        21327.5\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kCv110_cJiDz",
        "colab_type": "text"
      },
      "source": [
        "Don't worry about the above values because it's not important to know each and every one of them because it's just important to know how to use this technique in order to remove the outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igh_mRXeaJrI",
        "colab_type": "code",
        "outputId": "44660b71-2eb7-4387-c599-e527640a7c21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df = df[~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "df.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9191, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Nh93DCGJkqT",
        "colab_type": "text"
      },
      "source": [
        "As seen above there were around 1600 rows were outliers. But you cannot completely remove the outliers because even after you use the above technique there maybe 1–2 outlier unremoved but that ok because there were more than 100 outliers. Something is better than nothing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z87kHbgvaQbb",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WqFPRda8eEp_",
        "colab_type": "text"
      },
      "source": [
        "## 9. Plot different features against one another (scatter), against frequency (histogram)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-W6Q9-hJosZ",
        "colab_type": "text"
      },
      "source": [
        "### Histogram\n",
        "\n",
        "Histogram refers to the frequency of occurrence of variables in an interval. In this case, there are mainly 10 different types of car manufacturing companies, but it is often important to know who has the most number of cars. To do this histogram is one of the trivial solutions which lets us know the total number of car manufactured by a different company."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAnd4DSyeHDb",
        "colab_type": "code",
        "outputId": "44b04e39-9dc7-40fc-9ddb-b7182f4f6e1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        }
      },
      "source": [
        "df.Make.value_counts().nlargest(40).plot(kind='bar', figsize=(10,5))\n",
        "plt.title(\"Number of cars by make\")\n",
        "plt.ylabel('Number of cars')\n",
        "plt.xlabel('Make');"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4REREREaGBR4RERGRkWGBR0RERGRkWOARERERGRkWeERERERGhgUeERERkZFhgUdERERk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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c37WtYYWJuAQ",
        "colab_type": "text"
      },
      "source": [
        "### Heat Maps\n",
        "\n",
        "Heat Maps is a type of plot which is necessary when we need to find the dependent variables. One of the best way to find the relationship between the features can be done using heat maps. In the below heat map we know that the price feature depends mainly on the Engine Size, Horsepower, and Cylinders."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhHfLVTj6nmy",
        "colab_type": "code",
        "outputId": "50a65ae0-841a-42ec-87e6-1a99da1ea57b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "c= df.corr()\n",
        "sns.heatmap(c,cmap=\"BrBG\",annot=True)\n",
        "c"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>HP</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>MPG-H</th>\n",
              "      <th>MPG-C</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.326726</td>\n",
              "      <td>-0.133920</td>\n",
              "      <td>0.378479</td>\n",
              "      <td>0.338145</td>\n",
              "      <td>0.592983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HP</th>\n",
              "      <td>0.326726</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.715237</td>\n",
              "      <td>-0.443807</td>\n",
              "      <td>-0.544551</td>\n",
              "      <td>0.739042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>-0.133920</td>\n",
              "      <td>0.715237</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.703856</td>\n",
              "      <td>-0.755540</td>\n",
              "      <td>0.354013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MPG-H</th>\n",
              "      <td>0.378479</td>\n",
              "      <td>-0.443807</td>\n",
              "      <td>-0.703856</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.939141</td>\n",
              "      <td>-0.106320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MPG-C</th>\n",
              "      <td>0.338145</td>\n",
              "      <td>-0.544551</td>\n",
              "      <td>-0.755540</td>\n",
              "      <td>0.939141</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.180515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>0.592983</td>\n",
              "      <td>0.739042</td>\n",
              "      <td>0.354013</td>\n",
              "      <td>-0.106320</td>\n",
              "      <td>-0.180515</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Year        HP  Cylinders     MPG-H     MPG-C     Price\n",
              "Year       1.000000  0.326726  -0.133920  0.378479  0.338145  0.592983\n",
              "HP         0.326726  1.000000   0.715237 -0.443807 -0.544551  0.739042\n",
              "Cylinders -0.133920  0.715237   1.000000 -0.703856 -0.755540  0.354013\n",
              "MPG-H      0.378479 -0.443807  -0.703856  1.000000  0.939141 -0.106320\n",
              "MPG-C      0.338145 -0.544551  -0.755540  0.939141  1.000000 -0.180515\n",
              "Price      0.592983  0.739042   0.354013 -0.106320 -0.180515  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VEh8TS1mP8gDEXL2Gu5fnPxyZf+ronYfWXBXWnnAzzmixiqIh8u4dVEolXiVd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\nLNT5V6S5Qyg3ir4tfUSgrIzr8vgVy9Cvy88FcbmtXAkhhBCifDL3nKu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            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ROThOLQfRZw",
        "colab_type": "text"
      },
      "source": [
        "### Scatterplot\n",
        "\n",
        "We generally use scatter plots to find the correlation between two variables. Here the scatter plots are plotted between Horsepower and Price and we can see the plot below. With the plot given below, we can easily draw a trend line. These features provide a good scattering of points."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aOfHNFefSrX",
        "colab_type": "code",
        "outputId": "4b5286f0-5419-48bc-d2de-476aeb36f022",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "ax.scatter(df['HP'], df['Price'])\n",
        "ax.set_xlabel('HP')\n",
        "ax.set_ylabel('Price')\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h6NUgPKLU9q/Md0lNsUMZqamM0Pu0LUxg6x+0z/zN8/n7zEd+Ke/9eOeYTEa3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\nnNrUdXAtRZLn+MIP8K7Gqb4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hqTM6nvJd+MUr5G4qzMIPsK/hjJgKC81/5+K1DEvrYJZ5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            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQXy8o_gKFS5",
        "colab_type": "text"
      },
      "source": [
        "**Hence the above are some of the steps involved in Exploratory data analysis, these are some general steps that you must follow in order to perform EDA. There are many more yet to come but for now, this is more than enough idea as to how to perform a good EDA given any data sets. Stay tuned for more updates.**\n",
        "\n",
        "## Thank you."
      ]
    }
  ]
}